selectMultiKernel: Selection of tuning parameter for multivariate kernel...

In turgeonmaxime/multiKernel: Multivariate prediction using kernel-machine regression

Description

This function performs cross-validation for multivariate kernel regression and selects the optimal tuning parameter among a user-specified collection

Usage

selectMultiKernel(response, covariate, confounder = NULL,
  kernel = c("linear", "quadratic", "gaussian"), intercept = TRUE, tau_seq,
  K = 5, pure = FALSE, ...)

Arguments

response	matrix of response variables
covariate	matrix of covariate variables, which are included in the kernel.
confounder	matrix or data.frame of confounder variables, which are not included in the kernel.
kernel	Type of kernel to use.
intercept	Should we include an intercept?
tau_seq	Sequence of tuning parameters.
K	number of folds for cross-validation.
pure	Logical. Use the pure R version?
...	Extra parameters to be passed to the kernel function.

Value

Returns a list of kernel predictors, indexed by the different values of tau.

turgeonmaxime/multiKernel documentation built on June 1, 2019, 2:56 a.m.